Natasa Milic-Frayling, Microsoft Research Ben Shneiderman, Univ. of Maryland Marc A. Smith, Connected Action
• Input devices & strategies • Keyboards, pointing devices, voice • Direct manipulation • Menus, forms, commands • Output devices & formats • Screens, windows, color, sound • Text, tables, graphics • Instructions, messages, help • Collaboration & Social Media • Help, tutorials, training • Search www.awl.com/DTUI • Visualization Fifth Edition: 2010 1) E-commerce & National Priorities • Customer loyalty, community formation • Disaster response, community safety • Health, energy, education, e-government
2) Develop Theories of Social Participation • How do social media networks evolve? • How can participation be increased?
3) Provide Technology Infrastructure • Scalable, reliable, universal, manageable • Protect privacy, stop attacks, resolve conflicts
1) E-commerce & National Priorities • Customer loyalty, community formation • Disaster response, community safety • Health, energy, education, e-government
2) Develop Theories of Social Participation • How do social media networks evolve? • How can participation be increased?
3) Provide Technology Infrastructure • Scalable, reliable, universal, manageable • Protect privacy, stop attacks, resolve conflicts
Informal Gathering College Park, MD, April 2009
Article: Science March 2009
BEN SHNEIDERMAN
http://iparticipate.wikispaces.com NSF Workshops: Palo Alto & DC
www.tmsp.umd.edu
Community Informatics Research Network
intlsocialparticipation.net E-Commerce Social Media
911.gov • Residents report information • Professionals disseminate instructions • Resident-to-Resident assistance
Sending SMS message to 911, includes your phone number, location and time
Shneiderman & Preece, Science (Feb. 16, 2007) www.cs.umd.edu/hcil/911gov
911.gov Amber Alert • Residents report information • Professionals disseminate instructions • Resident-to-Resident assistance
Sending SMS message to 911, includes your phone number, location and time
Shneiderman & Preece, Science (Feb. 16, 2007) www.ncmec.org www.cs.umd.edu/hcil/911gov www.missingkids.com
911.gov Amber Alert • Residents report information • Professionals disseminate instructions • Resident-to-Resident assistance
Sending SMS message to 911, includes your phone number, location and time
Shneiderman & Preece, Science (Feb. 16, 2007) www.cs.umd.edu/hcil/911gov Health, Energy, Education,… Health, Energy, Education,… Health, Energy, Education,… 1) E-commerce & National Priorities • Customer loyalty, community formation • Disaster response, community safety • Health, energy, education, e-government
2) Develop Theories of Social Participation • How do social media networks evolve? • How can participation be increased?
3) Provide Technology Infrastructure • Scalable, reliable, universal, manageable • Protect privacy, stop attacks, resolve conflicts
Network Theories: Evolution models
• Random, preferential attachment,… • Monotonic, bursty,… • Power law for degree (hubs & indexes) • Small-world property • Forest fire, spreading activation,… • Matures, decays, fragments, …
Watts & Strogatz, Nature 1998; Barabasi, Science 1999, 2009; Newman, Phys. Rev. Letters 2002 Kumar, Novak & Tomkins, KDD2006 Leskovec, Faloutsos & Kleinberg, TKDD2007
Network Theories: Social science
• Relationships & roles • Strong & weak ties • Motivations: egoism, altruism, collectivism, principlism • Collective intelligence & action • Leadership & governance • Social information foraging
Moreno, 1938; Granovetter, 1971; Burt, 1987; Ostrom, 1992; Wellman, 1993; Batson, Ahmad & Tseng, 2002; Malone, Laubaucher & Dellarocas, 2009; Pirolli, 2009
Network Theories: Stages of participation
Wikipedia, Discussion & Reporting • Reader • First-time Contributor (Legitimate Peripheral Participation) • Returning Contributor • Frequent Contributor
Preece, Nonnecke & Andrews, CHB2004 Forte & Bruckman, SIGGROUP2005; Hanson, 2008 Porter: Designing for the Social Web, 2008 Vassileva, 2002, 2005; Ling et al., JCMC 2005; Rashid et al., CHI2006
From Reader to Leader: Motivating Technology-Mediated Social Participation
All Contributor Collaborator ` Leader Users Reader
Preece & Shneiderman, AIS Trans. Human-Computer Interaction1 (1), 2009 aisel.aisnet.org/thci/vol1/iss1/5/
1) E-commerce & National Priorities • Customer loyalty, community formation • Disaster response, community safety • Health, energy, education, e-government
2) Develop Theories of Social Participation • How do social media networks evolve? • How can participation be increased?
3) Provide Technology Infrastructure • Scalable, reliable, universal, manageable • Protect privacy, stop attacks, resolve conflicts
• Mobile, Desktop, Web, Cloud • 100% uptime, 100% secure • Giga-collabs, Tera-contribs
• Universal accessibility & usability • Trust, empathy, responsibility, privacy
• Leaders can manage usage • Designers can continuously improve Footprints of Human Activity
• Footprints in sand as Caesarea Preparation • Own the problem & define the schedule • Data cleaning & conditioning • Handle missing & uncertain data • Extract subsets & link to related information
• Integrates statistics & visualization
• 4 case studies, 4-8 weeks (journalist, bibliometrician, terrorist analyst & organizational analyst)
• Identified desired features, gave strong positive feedback about benefits of integration
www.cs.umd.edu/hcil/socialaction Perer & Shneiderman, CHI2008, IEEE CG&A 2009 http://www.youtube.com/watch?v=0M3T65Iw3Ac www.codeplex.com/nodexl www.codeplex.com/nodexl www.codeplex.com/nodexl https://wiki.cs.umd.edu/cmsc734_09/index.php?title=Homework_Number_3
I. Getting Started with Analyzing Social Media Networks 1. Introduction to Social Media and Social Networks 2. Social media: New Technologies of Collaboration 3. Social Network Analysis
II. NodeXL Tutorial: Learning by Doing 4. Layout, Visual Design & Labeling 5. Calculating & Visualizing Network Metrics 6. Preparing Data & Filtering 7. Clustering &Grouping
III Social Media Network Analysis Case Studies 8. Email 9. Threaded Networks 10. Twitter 11. Facebook 12. WWW 13. Flickr 14. YouTube 15. Wiki Networks
www.elsevier.com/wps/find/bookdescription.cws_home/723354/description Challenge: Requires Partitioning • Easy : Only need locally connected vertices e.g Vertex Degree, Eigenvector centrality
• Relatively Hard : Need local & some global graph knowledge e.g. Fruchterman-Reingold layout
• Hard : Need global graph knowledge at each node e.g. all pairs shortest paths -> betweenness centrality
Udayan Khurana
Implement and Measure Performance for Fruchterman-Reingold Layout Algorithm
GPU GeForce GTX 285, 1476 MHz, 240 cores Host CPU 3 GHz, Intel(R) Core(TM)2 Duo CUDA Graph Name #Nodes #Edges F-R run time F-R run time (seconds) (seconds) CA-AstroPh 18,772 396,160 84 1
cit-HepPh 34,546 421,578 344 1 John Locke Max Scharrenbroich soc-Epinions1 75,879 508,837 152 2 Puneet Sharma
soc-Slashdot0811 77,360 905,468 1578 3 Graphs from STANFORD’S SNAP Library soc-Slashdot0902 82,168 948,464 1781 3 (http://snap.stanford.edu/). Researchers who want to - create open tools - generate & host open data - support open scholarship
Map, measure & understand social media
Support tool projects to collection, analyze & visualize social media data.
THANKS !!! to Microsoft External Research http://www.flickr.com/photos/library_of_congress/3295494976/sizes/o/in/photostream/ http://www.flickr.com/photos/amycgx/3119640267/
Location, Location, Location Network of connections among “ecomm” mentioning Twitter users ecomm
Position, Position, Position • History: from the dawn of time! • Theory and method: 1934 -> • Jacob L. Moreno • http://en.wikipe dia.org/wiki/Jac ob_L._Moreno SNA 101
• Node A – “actor” on which relationships act; 1-mode versus 2-mode networks • Edge B – Relationship connecting nodes; can be directional C • Cohesive Sub-Group – Well-connected group; clique; cluster A B D E • Key Metrics – Centrality (group or individual measure) D • Number of direct connections that individuals have with others in the group (usually look at incoming connections only) • Measure at the individual node or group level E – Cohesion (group measure) • Ease with which a network can connect • Aggregate measure of shortest path between each node pair at network level reflects average distance – Density (group measure) • Robustness of the network • Number of connections that exist in the group out of 100% possible – Betweenness (individual measure) • # shortest paths between each node pair that a node is on • Measure at the individual node level F G • Node roles – Peripheral – below average centrality – Central connector – above average centrality C – Broker – above average betweenness H D I E http://en.wikipedia.org/wiki/Social_network
• Central tenet • Social structure emerges from • the aggregate of relationships (ties) • among members of a population • Phenomena of interest • Emergence of cliques and clusters • from patterns of relationships • Centrality (core), periphery (isolates), • betweenness • Methods Source: Richards, W. (1986). The • Surveys, interviews, observations, NEGOPY network analysis log file analysis, computational program. Burnaby, BC: analysis of matrices Department of Communication, Simon Fraser University. pp.7-16
(Hampton &Wellman, 1999; Paolillo, 2001; Wellman, 2001)y http://en.wikipedia.org/wiki/Centrality • Degree • Closeness • Betweenness • Eigenvector Social Media Network Roles
Welser, Howard T., Eric Gleave, Danyel Fisher, and Marc Smith. 2007. Visualizing the Signatures of Social Roles in Online Discussion Groups. The Journal of Social Structure. 8(2). [Local copy]
Experts and “Answer People” Discussion people, Topic setters
Discussion starters, Topic setters
• Leverage spreadsheet for storage of edge and vertex data • http://www.codeplex.com/nodexl Social Media Research Foundation
Open Tools
Open Data
Open Scholarship
A minimal network can illustrate the ways different locations have different values for centrality and degree Forthcoming, August 2010
Import from multiple social media network sources Social Media Research Foundation http://smrfoundation.org #facsumm at 9:30 AM Monday, July 12, 2010 #facsumm at 2:30 PM Monday, July 12, 2010 #microsoftresearch at 1:15 PM Monday, July 12, 2010 “Microsoft” at 6:00 AM Monday, July 12, 2010 “Microsoft” at 6:00 AM Monday, July 12, 2010 “Bing” at 2:30 AM Monday, July 12, 2010 “GOP” June 13, 2010 at 5:30PM “teaparty” at 1:00PM April 14, 2010 “Global Warming” at 6:00 PM Monday, May 7, 2010 “Global Warming” at 5:30 PM Monday, May 7, 2010 “WWW2010” at 10:30 PM Monday, April 28, 2010 “WWW2010” at 10:30 PM Monday, April 28, 2010 2010 - April - 12 - NodeXL - Twitter - CHI2010 X Log of Followers Y Log of Tweets
2010 - May - 7 - NodeXL - twitter global warming 2010 - May - 7 - NodeXL - twitter climate change Social Media Research Foundation http://smrfoundation.org